Variational Methods in Image Processing 1st Edition by Luminita Vese, Carole Le Guyader ISBN 1482262371 9781482262377

Variational Methods in Image Processing 1st Edition by Luminita A. Vese, Carole Le Guyader – Ebook PDF Instant Download/Delivery: 1482262371, 9781482262377

Full download Variational Methods in Image Processing 1st Edition after payment

 

 

Product details:

ISBN 10: 1482262371

ISBN 13: 9781482262377

Author:  Luminita A. Vese, Carole Le Guyader 

Variational Methods in Image Processing 1st Edition: Variational Methods in Image Processing presents the principles, techniques, and applications of variational image processing. The text focuses on variational models, their corresponding Euler–Lagrange equations, and numerical implementations for image processing. It balances traditional computational models with more modern techniques that solve the latest challenges introduced by new image acquisition devices. The book addresses the most important problems in image processing along with other related problems and applications. Each chapter presents the problem, discusses its mathematical formulation as a minimization problem, analyzes its mathematical well-posedness, derives the associated Euler–Lagrange equations, describes the numerical approximations and algorithms, explains several numerical results, and includes a list of exercises. MATLAB® codes are available online. Filled with tables, illustrations, and algorithms, this self-contained textbook is primarily for advanced undergraduate and graduate students in applied mathematics, scientific computing, medical imaging, computer vision, computer science, and engineering. It also offers a detailed overview of the relevant variational models for engineers, professionals from academia, and those in the image processing industry.

 

Variational Methods in Image Processing 1st Edition Table of contents:

• Introduction and Book Overview

  • Introduction
  • Overview

• Mathematical Background

  • Tikhonov Regularization of Ill-Posed Inverse Problems
  • Maximum a Posteriori (MAP) Estimate
  • Convolution
  • Fourier Transform
  • Topologies on Banach Spaces
  • Sobolev and BV Spaces
  • Calculus of Variations
  • Geometric Curve Evolution
  • Variational Level Set Methods
  • Numerical Analysis

• Image Restoration

  • Variational Image Restoration Models
  • Linear Degradation Model with Gaussian Noise and Total Variation Regularization
  • Numerical Results for Image Restoration
  • Compressive Sensing for Computerized Tomography Reconstruction

• Nonlocal Variational Methods in Image Restoration

  • Introduction to Neighborhood Filters and NL Means
  • Variational Nonlocal Regularization for Image Restoration
  • Numerical Results for Image Restoration

• Image Decomposition into Cartoon and Texture

  • Modeling
  • Numerical Results for Image Decomposition into Cartoon and Texture

• Image Segmentation and Boundary Detection

  • Mumford and Shah Functional for Image Segmentation
  • Description of the Mumford and Shah Model
  • Weak Formulation of the Mumford and Shah Functional: MSH1
  • Mumford and Shah TV Functional: MSTV

• Phase-Field Approximations to the Mumford and Shah Problem

  • Ambrosio and Tortorelli Phase-Field Elliptic Approximations
  • Shah Approximation to the MSTV Functional
  • Applications to Image Restoration

• Region-Based Variational Active Contours

  • Piecewise-Constant Mumford and Shah Segmentation Using Level Sets
  • Piecewise-Smooth Mumford and Shah Segmentation Using Level Sets
  • Applications to Variational Image Restoration with Segmentation-Based Regularization and Level Sets

• Edge-Based Variational Snakes and Active Contours

  • Snake Model
  • Geodesic Active Contours
  • Alignment Term
  • Topology-Preserving Snakes Model

• Applications

  • Nonlocal Mumford–Shah and Ambrosio–Tortorelli Variational Models
  • Characterization of Minimizers u
  • Gâteaux Derivative of Nonlocal M-S Regularizers
  • Image Restoration with NL/MS Regularizers
  • Numerical Discretizations
  • Experimental Results and Comparisons

• A Combined Segmentation and Registration Variational Model

  • Description of the Model
  • Implementation
  • Numerical Experiments

• Variational Image Registration Models

  • Introduction
  • A Variational Image Registration Algorithm Using Nonlinear Elasticity Regularization
  • Experimental Results

• A Piecewise-Constant Binary Model for Electrical Impedance Tomography

  • Introduction
  • Formulation of the Minimization
  • Numerical Details and Reconstruction Results

• Additive and Multiplicative Piecewise-Smooth Segmentation Models

  • Piecewise-Smooth Model with Additive Noise (APS)
  • Piecewise-Smooth Model with Multiplicative Noise (MPS)

• Numerical Methods for p−Harmonic Flows

  • Introduction
  • The S1 case
  • The S2 case
  • Numerical Experiments
  • Concluding Remarks and Discussions for More General Manifolds

 

People also search for Variational Methods in Image Processing 1st Edition:

variational image processing
    
a variational method in image recovery
    
bayesian-based iterative method of image restoration
    
variational methods in image processing uchicago
    
variational approach image processing

Tags:

Luminita Vese,Carole Le Guyader,Variational,Methods,Image,Processing